1. Field of the Invention
The present invention relates to the technical field of image processing and, more particularly, to an unmanned aerial vehicle image processing system and method.
2. Description of Related Art
Unmanned Aerial Vehicle (UAV) is an aircraft that can be remotely controlled or can fly autonomously based on pre-programmed flight plans or more complex dynamic automation systems. Comparing to the general air-borne or space-borne platform, UAV provides an innovative approach of remote sensing that is much cheaper, safer and more flexible to be deployed in a small area, ranged from a few to tens of square kilometers. As technologies advanced fast and cost decreased dramatically in the past few years, low-cost UAVs with competitive performance are now available commercially. Since all photos/data acquired by these low-cost UAVs need to be geo-referenced for further applications, to develop a fast and reliable approach to geo-referencing these photos/data plays one of the key roles of blooming the civilian market of low-cost UAV.
Rigorous approaches of geo-referencing photos/data acquired from the general airborne or spaceborne platforms have been made possible by the advances in photogrammetry and position/attitude sensors. The standard geo-referenced products, as a result, can be provided from these platforms on a regular basis. For the case of low-cost UAV platform, however, there is a significant gap in recording the accurate position and attitude data during its flight mission. The existing position/attitude sensors available are not only too heavy but also too expensive to be loaded on a low-cost UAV. As a result, a lot of low-cost UAV platforms are limited to qualitative applications using non-geo-referenced photos.
Another important characteristic of UAV is that the camera/sensor is not mounted on a rigid platform with the same light of sight well-calibrated all the time. In order to keep its mobility and flexibility, the camera/sensor module is usually assembled in the field and would be disassembled after the flight mission. The entire fuselage of a UAV would not be as rigid as the one of airborne or spaceborne platforms as well. Therefore, a constant boresight matrix (orientation offset) between the camera frame and the attitude sensor body frame is not a valid assumption for the case of UAV. The exterior orientation parameters of each photo in a low-cost UAV mapping system should be estimated individually in order to obtain a precise boresight matrix for high-accuracy planimetric mapping.
Therefore, it is desirable to provide an improved unmanned aerial vehicle image processing system and method to mitigate and/or obviate the aforementioned problems.